Package: mined 1.0-3

Dianpeng Wang

mined: Minimum Energy Designs

This is a method (MinED) for mining probability distributions using deterministic sampling which is proposed by Joseph, Wang, Gu, Lv, and Tuo (2019) <doi:10.1080/00401706.2018.1552203>. The MinED samples can be used for approximating the target distribution. They can be generated from a density function that is known only up to a proportionality constant and thus, it might find applications in Bayesian computation. Moreover, the MinED samples are generated with much fewer evaluations of the density function compared to random sampling-based methods such as MCMC and therefore, this method will be especially useful when the unnormalized posterior is expensive or time consuming to evaluate. This research is supported by a U.S. National Science Foundation grant DMS-1712642.

Authors:Dianpeng Wang and V. Roshan Joseph

mined_1.0-3.tar.gz
mined_1.0-3.tar.gz(r-4.5-noble)mined_1.0-3.tar.gz(r-4.4-noble)
mined_1.0-3.tgz(r-4.4-emscripten)mined_1.0-3.tgz(r-4.3-emscripten)
mined.pdf |mined.html
mined/json (API)

# Install 'mined' in R:
install.packages('mined', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 6 scripts 195 downloads 3 exports 2 dependencies

Last updated 2 years agofrom:654af11ce0. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 13 2024
R-4.5-linux-x86_64NOTENov 13 2024

Exports:LatticeminedSelectMinED

Dependencies:RcppRcppEigen